class_docstr: Base class for all MCMC `TransitionKernel`s.
  
  This class defines the minimal requirements to efficiently implement a Markov
  chain Monte Carlo (MCMC) transition kernel. A transition kernel returns a new
  state given some old state. It also takes (and returns) "side information"
  which may be used for debugging or optimization purposes (i.e, to "recycle"
  previously computed results).
  
  #### Example (random walk transition kernel):
  
  In this example we make isotropic Gaussian proposals of a given step size.
  
  ```python
  from tensorflow_probability.python.mcmc import kernel as kernel_base
  import tensorflow.compat.v2 as tf
  import tensorflow_probability as tfp
  
  tfd = tfp.distributions
  
  RWResult = collections.namedtuple("RWResult", 'target_log_prob')
  
  class RandomWalkProposalKernel(kernel_base.TransitionKernel):
    def __init__(self, target_log_prob_fn, step_size):
      self._parameters = dict(
        target_log_prob_fn = target_log_prob_fn,
        step_size = step_size)
  
    @property
    def target_log_prob_fn(self):
      return self._parameters['target_log_prob_fn']
  
    @property
    def step_size(self):
      return self._parameters['step_size']
  
    @property
    def is_calibrated(self):
      return False
  
    def one_step(self, current_state, previous_kernel_results, seed=None):
      scale = tf.broadcast_to(self.step_size, tf.shape(current_state))
      isotropic_normal = tfd.Normal(loc=current_state, scale=scale)
  
      next_state = isotropic_normal.sample(seed=seed)
      next_target_log_prob = self.target_log_prob_fn(next_state)
      new_kernel_results = previous_kernel_results._replace(
        target_log_prob = next_target_log_prob)
  
      return next_state, new_kernel_results
  
    def bootstrap_results(self, init_state):
      kernel_results = RWResult(
        target_log_prob = self.target_log_prob_fn(init_state))
      return kernel_results
  ```
docstr: Non-destructively creates a deep copy of the kernel.
  
  Args:
    **override_parameter_kwargs: Python String/value `dictionary` of
      initialization arguments to override with new values.
  
  Returns:
    new_kernel: `TransitionKernel` object of same type as `self`,
      initialized with the union of self.parameters and
      override_parameter_kwargs, with any shared keys overridden by the
      value of override_parameter_kwargs, i.e.,
      `dict(self.parameters, **override_parameters_kwargs)`.
docstr_tok: ["Non-destructively","creates","a","deep","copy","of","the","kernel",".","Args",":","*","*","override_parameter_kwargs",":","Python","String\/value","`","dictionary","`","of","initialization","arguments","to","override","with","new","values",".","Returns",":","new_kernel",":","`","TransitionKernel","`","object","of","same","type","as","`","self","`",",","initialized","with","the","union","of","self.parameters","and","override_parameter_kwargs",",","with","any","shared","keys","overridden","by","the","value","of","override_parameter_kwargs",",","i.e.",",","`","dict","(","self.parameters",",","*","*","override_parameters_kwargs",")","`","."]
code: def copy(self, **override_parameter_kwargs):
      """Non-destructively creates a deep copy of the kernel.
  
      Args:
        **override_parameter_kwargs: Python String/value `dictionary` of
          initialization arguments to override with new values.
  
      Returns:
        new_kernel: `TransitionKernel` object of same type as `self`,
          initialized with the union of self.parameters and
          override_parameter_kwargs, with any shared keys overridden by the
          value of override_parameter_kwargs, i.e.,
          `dict(self.parameters, **override_parameters_kwargs)`.
      """
      parameters = dict(self.parameters, **override_parameter_kwargs)
      new_kernel = type(self)(**parameters)
      return new_kernel
  
file_path: probability/tensorflow_probability/python/mcmc/kernel.py
imports: import abc
  import six
label_desc: Use image node_id 6 for calling the TransitionKernel obj's underlying member method code with example usage: obj.copy() and returns: new_kernel